Monocular Depth Estimation.
Monocular depth estimation is performed by generating a single depth value for every pixel in an image. As such, many monocular depth estimation methods are based on architectures used in well-studied pixel-to-pixel mapping problems such as semantic segmentation. As an example, fully convolutional networks (FCNs) [1]} were introduced for semantic segmentation, and were subsequently adopted for monocular depth estimation [2]}. The atrous spatial pyramid pooling (ASPP) module was also first proposed for semantic segmentation in DeepLab [3]}, [4]}, [5]} and subsequently used for depth estimation in DORN [6]} and BTS [7]}. Further, many methods jointly perform depth estimation and segmentation [8]}, [9]}, [10]}, [11]} in an end-to-end manner. We follow the design of the semantic segmentation network DeepLabV3 [5]} for estimating categorical depth distributions for each pixel in the image.
